Segmentation of heterogeneous document images : an ... - Tel
Segmentation of heterogeneous document images : an ... - Tel Segmentation of heterogeneous document images : an ... - Tel
dense printing, irregular spacing, side notes and varying text column widths. Apart from our results, we managed to apply Tesseract-OCR engine to both datasets. The Tesseract-OCR engine was one of the top 3 engines in the 1995 UNLV Accuracy test. Since then it has been improved by Google and is one of the most accurate open source OCR engines available. Currently, it is one of the most popular engines that has been used by many software and methods including EPITA method that holds the second place in Historical Document Layout Competition [4]. It is worth noting that the provided command line for Tesseract-OCR can only produce layout segmentation results in hOCR format, an open standard which defines a data format for representation of OCR output by embedding this data into a standard HTML format. tel-00912566, version 1 - 2 Dec 2013 Evaluation of the results is carried out by Prima Layout Evaluation Tool [25]. The parameters of the evaluation are configured to measure the pure segmentation performance. Therefore, missing and partial missing text lines are considered worst and having the highest weights of 1. The weights for merge and split errors are set to 0.5, whereas false detected text lines, as the least important error type, has a weight of 0.1. Appropriately, the errors are also weighted by the size of the affected area (excluding background pixels). In this way, a partially missed text line, having one or two missed characters, has less influence on the overall result than missing of a whole text line. These settings are taken from [4] for the evaluation of paragraph detection; however, they are also reasonable for evaluation of text line detection. Table 5.1: LINE DETECTION SUCCESS RATES FOR 61 DOCUMENTS OF ICDAR2009 DATASET Area weighted error % Area weighted % Count weighted % Method Merge Split Miss Partial miss False detection Success Success Our Method 5.73 4.69 3.13 4.63 3.89 93.57 71.99 Tesseract 7.53 0.95 3.32 1.67 2.00 95.01 80.21 Table 5.2: LINE DETECTION SUCCESS RATES FOR 100 DOCUMENTS OF ICDAR2011 DATASET Area weighted error % Area weighted % Count weighted % Method Merge Split Miss Partial miss False detection Success Success Our Method 09.35 3.37 0.21 1.65 0.85 95.53 69.52 Tesseract 29.92 7.92 0.83 0.68 3.72 85.46 51.03 Table 5.3: LINE DETECTION SUCCESS RATES FOR 100 DOCUMENTS OF OUR CORPUS Area weighted error % Area weighted % Count weighted % Method Merge Split Miss Partial miss False detection Success Success Our Method 04.80 6.96 0.42 3.23 2.28 95.04 77.43 Tesseract 14.18 2.29 0.96 2.12 6.52 92.02 66.21 The majority of errors in our method happen because of the segmentation results of region detection from the previous stage. Errors due to merging of 98
text lines can be divided into two categories. Merging of adjacent text lines from different columns are due to errors of region segmentation. Currently the most occurring scenario that could contribute to this error is that in some documents, pieces of broken rule lines that are supposed to separate two columns of text, are mistaken for ’I’,’i’ and ’l’ and remain as text components. These misclassified components merge two columns of text in region detection. In this situation text line separators from text line detection stage of the method extends to the boundaries of the region and eventually text lines from different columns of text are merged. Merging of parallel adjacent text lines that are located on top of each other is an error that may happen due to text line detection algorithm where two text lines are intertwined. However chances of this happening is very slim. tel-00912566, version 1 - 2 Dec 2013 Table 5.1 shows that the Tesseract-OCR engine performs a slightly better job on clean on clean and well-formatted documents. However, tables 5.2 and 5.3 indicate that as datasets move toward historical documents with handwritten text lines, our method perform significantly better than Tesseract-OCT. 99
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text lines c<strong>an</strong> be divided into two categories. Merging <strong>of</strong> adjacent text lines from<br />
different columns are due to errors <strong>of</strong> region segmentation. Currently the most<br />
occurring scenario that could contribute to this error is that in some <strong>document</strong>s,<br />
pieces <strong>of</strong> broken rule lines that are supposed to separate two columns <strong>of</strong> text, are<br />
mistaken for ’I’,’i’ <strong>an</strong>d ’l’ <strong>an</strong>d remain as text components. These misclassified<br />
components merge two columns <strong>of</strong> text in region detection. In this situation<br />
text line separators from text line detection stage <strong>of</strong> the method extends to the<br />
boundaries <strong>of</strong> the region <strong>an</strong>d eventually text lines from different columns <strong>of</strong> text<br />
are merged. Merging <strong>of</strong> parallel adjacent text lines that are located on top <strong>of</strong><br />
each other is <strong>an</strong> error that may happen due to text line detection algorithm<br />
where two text lines are intertwined. However ch<strong>an</strong>ces <strong>of</strong> this happening is very<br />
slim.<br />
tel-00912566, version 1 - 2 Dec 2013<br />
Table 5.1 shows that the Tesseract-OCR engine performs a slightly better<br />
job on cle<strong>an</strong> on cle<strong>an</strong> <strong>an</strong>d well-formatted <strong>document</strong>s. However, tables 5.2 <strong>an</strong>d<br />
5.3 indicate that as datasets move toward historical <strong>document</strong>s with h<strong>an</strong>dwritten<br />
text lines, our method perform signific<strong>an</strong>tly better th<strong>an</strong> Tesseract-OCT.<br />
99
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